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Snow is a major component of the hydrological cycle. Many important natural phenomena, such as the behavior of climate and the availability of water resources, show a strong relationship with snow water equivalent (SWE)and snow extent, especially in mountainous regions like the Columbia River basin. While hydrological models predict these quantities, model biases and the uncertainties of input data can lead to large errors in results. Meanwhile, remote sensing observations, such as passive microwave brightness temperatures, provide accurate estimates of snow characteristics, but do not measure SWE directly and have lower temporal resolution than hydrological models. Data assimilation can combine the strengths of both types of estimation by periodically updating model forecasts with remote sensing observations. However, since satellite observations measure snow brightness temperatures and hydrological models predict SWE, we must convert these estimates to a common form before assimilation. One way to do this is to convert predicted SWE into brightness temperatures via a radiative transfer model. Here we compare the performance of two radiative transfer models, Land Surface Microwave Emission Model1 (LSMEM) and the Dense Media Radiative Transfer2 (DMRT) model, in assimilating remotely-sensed observations of snow into SWE predicted by the SNTHERM.893 hydrological model in the Columbia River basin.

Simulated (SNTHERM/LSMEM before and after assimilation) and observed (AMSR-E) high-altitude brightness temperatures over the Stanley Basin (43.60 N, 114.67 W), for 18.7 GHz h/v, and 36.5 GHz h/v (a-d, respectively). Assimilation begins on Jan 1, 2003. Note that a more sophisticated estimate of the variance of the AMSR-E measurements could potentially bring predicted brightness temperatures closer to observations.

Simulated (SNTHERM before and after assimilation) and observed (SNOTEL) snow water equivalent in the Stanley Basin (43.60 N, 114.67 W). Assimilation begins on Jan 1, 2003. Assimilation lowers the predicted snow water equivalent, bringing it closer to the observations. Note that a more sophisticated estimate of the variance of the AMSR-E measurements could potentially bring predicted SWE even closer to observations.

Observations taken from the Local Scale Observation Site (LSOS) of the Cold Land Process Experiment (CLPX) in Fraser Park, CO:

While this study is still in its preliminary stages, evidence so far suggests that:

Assimilation of passive microwave brightness temperatures into a hydrological model via a radiative transfer model such as LSMEM or DMRT can improve estimates of snow pack properties such as snow depth and snow water equivalent, both at sites where observations exist and in areas where observations are sparse.

Care must be taken when comparing predicted snow properties to those observed at SNOTEL sites; SNOTEL observations are point measurements, while AMSR-E measurements and our input meteorological forcings are areal averages. AMSR-E brightness temperatures are influenced by heterogeneous land cover and fluctuations of moisture content in the intervening atmosphere, while point measurements on the ground are not influenced by these.

Future Work:

Data assimilation with the DMRT model

More sophisticated estimation of observation errors

Examination of multiple sites around the Columbia Basin, including sites with extensive forest cover

Simulated (LSMEM) and observed (AMSR-E) high-altitude brightness temperatures at LSOS, based on snow properties predicted by SNTHERM, for 18.7 GHz h/v, and 36.5 GHz h/v (a-d, respectively). The abrupt dip on 3/20 coincides with a large deposition and melting event.